Method of detecting and classifying objects by means of radar

ABSTRACT

The present invention relates to a method of detecting and classifying objects using radar. The method means that a broadband radar signal comprising wavelengths that coincide with characteristic lengths of targets to be detected and classified is transmitted. Further, a returned echo signal is received and analysed, at which a signal analysis in a combined time-frequency domain is used, which comprises determining characteristic frequencies in the signal return and their mutual time relations. Finally, a comparison with the corresponding stored values of an analysis of possible targets is performed.

BACKGROUND OF THE INVENTION

Field of the Invention

1. The present invention relates to a method of detecting andclassifying objects using radar.

Description of the Related Art

2. There are large areas in Asia, Africa, and the Middle East coveredwith mines. At a rough estimate, 100 million mines are laid in more than60 countries. Today there does not exist any technology capable ofdetecting these mines quickly and with large probability. As an example,it has been estimated that with the present pace of clearance 2500 yearswould be needed to clear Afghanistan of mines.

One problem is all metal fragments surrounding the mines in the ground.For each mine it finds, a metal detector on average also detects athousand fragments. If the false alarm rate could be reduced to only ahundred per mine, the clearance pace would be increased ten times.Another problem is the non-metallic mines, which are very hard to detectusing conventional techniques.

Earlier attempts at classifying detected targets using radar have beenconcentrated toward air targets. The predominant method has been tocompare the amplitude of the returned echo signal for one or morepolarizations. The method is aspect dependent, which involves largeamounts of data with ensuing handling problems. Systems tend to beeither unmanageable or capable of classifying only a small amount oftargets or both.

SUMMARY OF THE INVENTION

The present invention solves the problem in question, viz., to be ableto classify a large amount of different targets, independently of theaspect angle of the target by designing it as the corresponding storedvalues of an analysis of possible targets. Convenient realizations ofthe invention include basing the analysis on the first portion of thereturned signal, including the specular reflex, as well as its laterportion, including the returned radiation from creeping waves induced inthe target and, where appropriate, reflexes from the interior of thetarget, such as its rear edge. In addition, captured signals may beamplified with increasing amplification according to the distance beforethey are analyzed, and mines buried in the ground or targets in theatmosphere may be chosen as intended targets.

BRIEF DESCRIPTION OF THE DRAWING

In the following the invention will be described referring to encloseddrawings, in which:

FIG. 1a shows a block conceptual diagram of detection and classificationaccording to the invention,

FIG. 1b shows examples of the contents of three of the blocks of FIG.1a, and

FIG. 2 shows a more detailed diagram of the data flow in a deviceaccording to the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

First, the theoretical background will be outlined. A radar pulse thatis incident on a target interacts with the target, e.g., an aircraft ora mine buried in the ground. The returned pulse carries informationabout the target that can be used for target identification.

The target information inherent in the returned pulse is caused mainlyof the following three effects:

1. Specular reflection from those boundary surfaces of the target thatare facing the radar antenna. As a general rule, this is the mostprominent contribution to the backscattered pulse. The size as well asthe geometric shape of the target determines in general the waveform ofthe specular reflection (multiple reflection may also occur).

2. Creeping waves, or surface waves, are induced on the boundarysurfaces of the target. These waves circumscribe the target as theycontinuously radiate off curved surfaces and edges. A portion of theradiated creeping waves is returned to the receiving antenna.

3. Internal reflections from i.a. the rear edge of the target, if thetarget (at least in part) is composed of dielectric material(s). In suchcase, a portion of the incident radar pulse penetrates these material(s)and conducted through the target i.a. to the rear edge.

The signature information in a radar return depends to a large extent onthe frequency content of the transmitted radar pulse. Above all, thesignature information depends on how the corresponding wavelengths arerelated to characteristic lengths of the target. Ideally, the incidentwaveform should contain wavelengths of the same order of magnitude asthe characteristic lengths of the target. On that account, it isimportant that an ultra-wideband radar system is used. By using animpulse radar that transmits extremely short pulses (ns) or by usingstepped frequencies for the transmitted pulse, the ultra-wideband iscreated.

The receiving antenna picks up the returned echo whose time-evolution iscomposed of the above-described components. For a given target, thereturned wave-form evolves in time in a characteristic way, and, usingsuitable signal analysis, generates a signature of the target in thepresent aspect. For many targets, the aspect dependence of the computedsignature is relatively small or varies in a manageable way with theaspect. This implies that only a small number of signature templateswould be necessary for target identification.

An essential part of the invention is that the classification of targetsis based on signal analysis in the combined time-frequency domain, atwhich characteristic frequencies together with their mutual relations intime are determined, which becomes input data for the classification. Asuitably chosen time-frequency distribution (TFD) generates targetsignatures that are composed of distinguishing features in thetwo-dimensional time-frequency domain. The features generated by a TFDcomprise more target-distinguishing features than do the featuresgenerated by standard Fourier transform technique. In particular, TFDshave proved advantageous for application to targets buried in theground, where clutter from the ground above a buried target can beremoved while keeping the target information.

When a signal is examined both the front portion of the returnedwaveform (the specular return) and the later portion (returned radiationfrom creeping waves) are used.

To give a clear idea of the inventive method, it will be presented inthree steps using progressively more details. First as a blockconceptual diagram in FIG. 1, then a diagram of the data flow is givenin FIG. 2. In a third step a concrete example is given.

Large amounts of data are captured by the receiving system. To rendertarget classification possible this amount must be reduced. This iscarried out during the pre-processing. Extraneous data are removed andrelevant data extracted. At the detection, objects are distinguishedfrom ground-related data. The features of the objects are computed andcompared with a reference library leading to a classification. Thecomputer screen displays a depth view of the ground together with thedetected objects and indications of their respective class belongings.The block conceptual diagram is displayed in FIG. 1a.

The data flow and a suitable implementation of its signal processingwill now be outlined with reference to FIG. 2. As mentioned before, thecaptured data must be considerably reduced and pre-processed in severalsteps to generate an unambiguous picture on the screen and to rendertarget classification possible. The pre-processing for the display isinitiated by subtracting a representative background signal andenhancing the relevant part of the backscattered echo. This is reducedby resampling, using a lower sampling rate. Then the signal is lowpassfiltered. A possible DC component in the signal is subtracted. Toimprove the signal-to-noise ratio, averaging is also performed. Targetsburied to large depths return weak echoes, which can be enhanced usingdistance dependent amplification. Finally, the absolute value of thesignal is computed and color-coded regarding signal amplitude. Theprocessing is then finished and data are shown on the screen in desiredmode.

Simultaneously with the screen processing, the signal is pre-processedfor the detector. Its purpose is to bring out the object informationfrom the background. From the returned echo signal a previously storedcompound signal is subtracted whose individual components willcompensate i.a. for system errors, surface and sub-surface echoes fromthe ground. Various detection methods are available. The moststraightforward method uses a fixed threshold. If the maximum value ofthe signal exceeds the threshold value, detection of a target isdeclared. Another method is to use an envelope based on a representativebackground signal. Detection of a target is then declared when thecaptured signal anywhere extends outside the envelope. In this way,objects buried to large depths giving a weak signal amplitude can bemore effortlessly detected. It is also possible to use an adaptivethreshold that slowly adapts to the prevailing ground conditions whileit is sensitive to swift changes in the various portions of the capturedsignals.

When a target has been detected, the corresponding data set is sent tothe classification. Here the task is to obtain the unique parameters ofthe object in question. This is generally called feature extraction. Theinvention bases the classification on some significant combinations ofpoints of time and frequencies as parameters. Wavelets and pseudo-Wignerdistributions are examples of two TFD methods of signal processing.

Alternatively, the various components of returned radar echoes could beexamined with respect to their different rates of damping, which dependon the material properties of the target. These damping rates can beutilized for classification.

Extracted features are subsequently compared with information stored ina reference library. The comparison can be performed in several ways. Awell-known method is to use neural networks. Other methods are thenearest neighbour method, Bayes decision rule, and pattern recognition.Any one of these methods can provide a target classification with somelikelihood.

The information is presented on a computer screen in various modes. Itcan change between raw data, detected objects, and classificationresults, or any combination of them.

In what follows, a particular example of the implementation of detectionand classification is detailed. The impulse radar system is based onunits manufactured by ERA Technology, England. The radar system is builton a transmitting unit and a receiving unit. The transmitter generates avery short pulse with a pulse repetition frequency of 200 kHz. Theduration of the pulse is only 0.3 ns, which makes the required bandwidthpossible. The transmitted peak power is 18 W. The antenna unit isequipped with two dipole antennas, one transmitting and one receiving,at right angles to each other. The radar system has a bandwidth of about1.7 GHz (300 MHz-2 GHz). Sampling of signals is performed using aTektronix TDS 820, which has an analog bandwidth of 6 GHz. The samplingfrequency is 20 GHz, and the result is obtained with a signal levelresolution of 14 bits. The sampling is controlled by a PC, whichreceives the data for storing, processing, and presentation.

For the screen presentation, the following data processing is performed.Each waveform is composed of 500 samples. The clutter level is reducedby subtracting from each captured waveform a previously stored waveformthat consists of system errors and a ground-reflex reference. Thewaveform is filtered by mixing 25% of a new sample with 75% of thepreviously computed sample. The 500 samples are then reduced to 250 bykeeping every other.

Subtracting the mean value of the 250 samples from each sampleeliminates a possible DC level.

To smooth out the variations between the sampled waveforms and renderthe presentation unambiguous, a running averaging is performed. Each newwaveform is weighted by 25% of the preceding averaged waveform.

Deeply buried objects give weak returned echo signals. They can beenhanced by applying a distance dependent amplification. This can bechosen as ${F = {1 + {\frac{i - 50}{200}*P}}},$

where F is the amplification, i the number of samples (50-250), and theparameter P is in the interval (0-15).

For dry sand the value of P=12 gives the best result.

Subsequently, the absolute value of the waveform is computed to accountappropriately for the negative samples. Finally, the signal amplitude ofthe waveform is colour-coded, which causes strong target echoes to standout distinctly against the background. By preference, the result ispresented as a depth-view of the ground.

At the same time as the above signal processing for the screenpresentation runs, the processing for detection and classification isperformed. Each waveform comprises 500 samples. To detect weak echoes astored waveform consisting of system errors and a ground-reflex issubtracted from each captured waveform. The resulting waveform consistsof noise and, possibly, an echo returned from an object.

The threshold level for the detector is determined by the maximum valueof the last 20 samples of each waveform to which 8 is added, whichbrings the threshold level just above the noise level.

Of the 500 captured samples in the waveform, only the first 256 ones aresubsequently used. The remaining samples are lowpass filtered using aChebyshev filter of order 8. Afterwards, a down sampling from 256 to 128samples is performed by keeping every other sample.

In case of detection of a target, the classification procedure isinitiated. To enhance the likelihood of correct classification both timeand frequency are taken into account. The classification is based oncoefficients computed using Linear Phase Daubechies Wavelets. Thealgorithm performs time and frequency analysis of the waveform.Outputted data consist of 200 coefficients, which are compared with thereference library. Each object in the library is defined by 15coefficients. Position and size of the coefficients are compared, andthe least deviating sum of target coefficients and reference datacoefficients determines the classification of the object, which is thendisplayed on the screen.

What is claimed is:
 1. An essentially aspect independent method ofdetecting and classifying known and unknown targets using radar,comprising the steps of: transmitting a wideband radar signal thatincludes wavelengths which coincide with characteristic overall lengthsof targets to be detected and classified so as to obtain targetsignature information having limited aspect dependence; receiving theradar signal as a returned signal; detecting and determiningcharacteristic frequencies in the returned signal as well as mutual timerelations of said characteristic frequencies; storing a plurality ofvalues for characteristic frequencies and corresponding mutual timerelations for a plurality of known targets in an analyzer; and analyzingthe characteristic frequencies and mutual time relations in the returnedsignal by comparing the returned signal characteristic frequencies andmutual time relations with the stored characteristic frequencies andmutual time relations values of known targets to identify the unknowntarget.
 2. The method according to claim 1, wherein returned signals areamplified with higher amplification for signals that have traveled agreater distance than for signals that have traveled a shorter distance.3. The method according to claim 1, wherein the step of analyzingincludes consideration of a first portion of the returned signal,including specular reflection, and a later portion of the returnedsignal, including returned radiation from surface waves induced in thetarget.
 4. The method according to claim 3, wherein the target iscomposed of dielectric material and the analysis is further based onreflexes from an interior of the target.
 5. The method according toclaim 4, wherein the interior of the target includes a rear edge of thetarget.
 6. The method according to claim 3, wherein the step ofanalyzing includes consideration of reflexes from an interior of thetarget when the target is of dielectric material.
 7. The methodaccording to claim 6, wherein the interior of the target includes a rearedge of the target.
 8. The method as set forth in claim 1, wherein thetargets to be detected include an aircraft and the wideband radar signalincludes a frequency having a wavelength corresponding to acharacteristic length of said aircraft.
 9. The method as set forth inclaim 1, wherein the targets to be detected include a buried mine andthe wideband radar signal includes a frequency having a wavelengthcorresponding to a characteristic length of said buried mine.
 10. Themethod as set forth in claim 1, wherein the wideband radar signal has abandwidth of approximately 300 MHz to 2 GHz.
 11. An essentiallyaspect-independent method of detecting and classifying a target usingradar comprising: transmitting a wideband radar signal that includeswavelengths which coincide with characteristic lengths of targets to bedetected and classified; receiving a returned radar signal containingspecular reflection from target boundary surfaces facing the transmittedradar signal, and surface waves induced on the boundary surfaces whichcircumscribe the target; detecting frequencies and mutual time relationsin the returned radar signal; and analyzing and identifying the targetby comparing the detected frequencies and mutual time relations withstored values for characteristic frequencies and corresponding mutualtime relations for a plurality of known targets.
 12. The methodaccording to claim 11, wherein buried ground mines are chosen asintended targets.
 13. The method according to claim 11, wherein targetsin earth's atmosphere are chosen as intended targets.
 14. The methodaccording to claim 11, wherein the step of analyzing includesconsideration of a first portion of the received radar signal, includingspecular reflection, and a later portion of the received signal,including returned radiation from surface waves induced in the target.15. The method according to claim 14, wherein the step of analyzingincludes consideration of reflexes from an interior of the target whenthe target is of dielectric material.
 16. The method according to claim15, wherein interior of the target includes a rear edge of the target.